Abstract
The standard computational formula for the three-stage least-squares estimator is a daunting affair even for modest sized systems of equations. Through the use of the QR decomposition, however, these computations can be substantially reduced in size, removing the order of T (number of observations) from the relevant dimensions. This produces a set of calculations and memory requirements far more accommodating to all users of 3SLS, but particularly to those who may wish to include this estimator in their home-made arsenal without having to engage in special programming techniques.
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References
Belsley, D.A., Kuh, E., and Welsch, R.E. (1980) Regression Diagnostics: Identifying Influential Data and Sources of Collinearity, Wiley, New York.
Golub, G.H. and Van Loan, C.F. (1983) Matrix Computations, Johns Hopkins University Press, Baltimore.
Sargan D. (1988) Lectures on Advanced Econometric Theory, Blackwell, New York.
Stewart, G.W. (1973) Introduction to Matrix Computations, Academic Press, New York.
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Belsley, D.A. Paring 3SLS calculations down to manageable proportions. Computer Science in Economics and Management 5, 157–169 (1992). https://doi.org/10.1007/BF00426758
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DOI: https://doi.org/10.1007/BF00426758